Job Summary Otsuka is looking for a strong Clinical Statistician in the Medical and Real World Data Analytics organization to provide statistical expertise for analysis of clinical trials data beyond the study endpoints, including secondary, exploratory, subgroup analyses. The overall purpose is to support evidence generation towards design new trials, assessing economic value, supporting health technology assessments through statistical analyses of clinical and other data. The role will manage the timely execution of statistical and research methodological components to meet project objectives and has a thorough understanding of current requirements for statistical standards. Otsuka is a dynamic organization where motivated individuals come together to create new products for better health. We're driven by our purpose: defying limitations to improve lives. Job Description Key Responsibilities: Work closely with internal data scientists and data programmers, and external stakeholders including Medical Affairs, Value and Real World Evidence to provide end-to-end statistical expertise and delivery of analyses. Actively participate in project/study team meetings. Develop statistical sections of protocols; participate in mock-ups, review, and approve tables, listings and graphs specifications. Closely work with Medical Affairs to support statistical design, conduct secondary analyses of clinical and other external data. Provides statistical consultation for ad hoc analysis requests including design of appropriate analyses to answer relevant questions. Work closely with clinicians, programmers, other statisticians, data scientists, and publication managers to generate QC outputs for publications, review publications to ensure accuracy, quality and soundness of statistical methodologies; coordinate, oversee and ensure quality delivery from vendors and their resources. Design statistical analysis, conduct statistical analysis and interpretation of analyses results. Ensure quality and timelines are met. Participate in study concept, protocol and SAP development for Real World Studies and ensure appropriateness of study design, sample size and statistical methodologies proposed. Author analysis plans, including table, figure, listing shells, and review reports. Develop cross-functional data review plan, key reports, and data dissemination plan in order to facilitate the review, summary, and dissemination of key study data/results. Contribute to external interactions with regulators, payers, review boards, etc. Qualifications Required: A PhD or Masters in statistics, biostatistics, or a related quantitative field. 7+ (PhD)/10+ (masters) years’ experience in pharmaceutical/biotechnology/CRO industry. Strong statistical background demonstrated by serving in primary roles as statistician or lead statistician in trial design and analyses. Experienced in developing statistical analyses plans and possesses a strong interest to learn and develop novel statistical and epidemiological methods to support evidence generation. Solid understanding of clinical research data standards, e.g. CDISC, ADaMs/STDMs. Experienced with statistical programming in SAS, R or Python. Deep understanding of data and analyses to convert Real World Data to evidence, including data from clinical trials, healthcare utilization datasets in US and EU, and Japan. Demonstrated ability to explain complex ideas and statistical results to diverse audiences ranging from executives to technical programmers. Excellent organizational skills with an ability to embrace change and effectively manage portfolio. Ability to present and defend statistical analyses at Industry forums and regulatory agencies. Experience supporting development or RWD analytics portfolio spanning multiple products, therapeutics areas (e.g. CNS, renal, digital solutions, etc.). Strong technical writing, editing, and communication skills along with collaborative mindset. Preferred: Experience with design and analyses of observational studies, Health Economic data and research is strongly preferred. Experience with latest statistical methods and Data Science. Experience with mathematical modeling and simulation, epidemiology, health economic models.